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Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Related publications (59)

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Computational models of intrinsic motivation for curiosity and creativity

Wulfram Gerstner, Alireza Modirshanechi, Sophia Becker

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...

2024

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits.In the ...

EPFL2023

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Renato Zanetti

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of ...

2023

Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease

Yujian Diao

Magnetic resonance imaging (MRI) has been a valuable tool in investigating the pathological cascade of Alzheimer's disease (AD) and its progression, which are still open questions. Although some MRI-derived hallmarks in terms of functional connectivity and ...

EPFL2023

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Friedhelm Christoph Hummel, Claudia Bigoni, Nima Taherinejad

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characte ...

NATURE PORTFOLIO2023

Algorithms for Efficient and Robust Distributed Deep Learning

Tao Lin

The success of deep learning may be attributed in large part to remarkable growth in the size and complexity of deep neural networks. However, present learning systems raise significant efficiency concerns and privacy: (1) currently, training systems are l ...

EPFL2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Maryam Kamgarpour, Andreas Krause

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022